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A deep learning-based recognition method for degradation monitoring of ball screw with multi-sensor data fusion

机译：基于深度学习的多传感器数据融合滚珠丝杠劣化监测识别方法

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摘要

In this paper, a novel intelligent ball screw degradation recognition method based on deep belief networks (DBN) and multi-sensor data fusion is proposed. First, the derived method calculates frequency spectrums of raw signals, and the fused frequency spectrums are calculated by the multi-sensor data fusion. Then, a deep learning based recognition model that can estimate the degradation condition of ball screw automatically is established with the fused dataset. The effectiveness of the proposed method is validated using dataset collected from the degradation test of ball screw. The dataset contains massive samples involving 7 degradation stages under 9 working conditions by 3 accelerometers. The classification results indicate that the proposed DBN-based method is able to mine intrinsic characteristics from the fused frequency spectrums adaptively and obtain a superior recognition accuracy. Finally, two comparative studies are performed to show the advantage of the proposed DBN-based method in ball screw degradation condition recognition. (C) 2017 Elsevier Ltd. All rights reserved.
机译：提出了一种基于深度置信网络（DBN）和多传感器数据融合的智能滚珠丝杠退化识别方法。首先，导出的方法计算原始信号的频谱，并通过多传感器数据融合来计算融合的频谱。然后，利用融合后的数据集建立了一个基于深度学习的识别模型，该模型可以自动估计滚珠丝杠的退化状况。使用从滚珠丝杠的降解测试中收集的数据集验证了该方法的有效性。该数据集包含3个加速度计在9个工作条件下涉及7个降解阶段的大量样本。分类结果表明，所提出的基于DBN的方法能够自适应地从融合频谱中挖掘出固有特征，并获得较高的识别精度。最后，进行了两项比较研究，以显示所提出的基于DBN的方法在滚珠丝杠退化条件识别中的优势。 （C）2017 Elsevier Ltd.保留所有权利。

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  • 来源
    《Microelectronics & Reliability》 |2017年第8期|215-222|共8页
  • 作者

    Zhang Li; Gao Hongli; Wen Juan; Li Shichao; Liu Qi;

  • 作者单位

    Southwest Jiaotong Univ, Sch Mech Engn, Chengdu 610031, Sichuan, Peoples R China;

    Southwest Jiaotong Univ, Sch Mech Engn, Chengdu 610031, Sichuan, Peoples R China;

    Southwest Jiaotong Univ, Sch Mech Engn, Chengdu 610031, Sichuan, Peoples R China;

    Southwest Jiaotong Univ, Sch Mech Engn, Chengdu 610031, Sichuan, Peoples R China;

    Southwest Jiaotong Univ, Sch Mech Engn, Chengdu 610031, Sichuan, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Deep learning; Deep belief networks; Multi-sensor data fusion; Ball screw; Degradation recognition;

    机译：深度学习;深度信念网络;多传感器数据融合;滚珠丝杠;退化识别;

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